Control systems for controlling processes are known in the art. Control system requirements have increased in recent years. It was found that a PID controller is not good enough in its current form to handle those requirements. Therefore, the use of nonlinear controllers had become a must. Even though the nonlinear controller is very complicated and cannot be applied on some systems.
A problem in a PID controller is that it has a number of control loops (often three or four loops) and for each loop, there are three parameters that needs tuning which may need days of tuning to reach an acceptable performance. Even after all that tuning effort, the performance may still not matching the performance of a nonlinear controller. Some controller allow a reduction of the number of tuning parameters (Deadbeat, Zeigler-Nichols) but without being able to make any change on the number of control loops.
In 1980s, a deadbeat controller was developed in order to decrease the number of tuning parameters. However, it was only applicable with discrete time control. In 2008, an application with a deadbeat controller was presented which allowed continuous time control. See, for instance, Peng Wen and Te-Wei Lu, Decoupling Control of a Twin Rotor MIMO System using Robust Deadbeat Control Technique, proceeding of: Control and Automation, 2007. ICCA 2007. IEEE according to which it “studies the decoupling control of a twin rotor MIMO system and proposes to apply robust deadbeat control technique to this nonlinear system. Firstly, the nonlinear problem is identified and system model is developed. Then we show that the system is able to be decoupled into two SISO systems, and the crossing couplings can be considered as disturbances to each other. Finally we apply a robust deadbeat control scheme to the two SISO systems and design a controller for each of them. This design is evaluated in simulations, and the final result is tested in a twin rotor MIMO system. Comparing with a traditional system with two PID controllers, this method is easy to follow, and the results show that the proposed scheme has less overshoot, shorter settling time and more robust to crossing coupling disturbances.” Thus, a deadbeat controller was applied in continues form on a Multi Input Multi Output (MIMO) system. Nonetheless, it was applied on modified system transfer function. Therefore, it is considered insufficient since any modification on the system usually lead to high margin of error. The publication does not explain the system, and a system order reduction was applied as the real system (which is not true) changing any part of the system is basically ignoring a part of that system like if it does not exist. System mathematical model is a representation of the system component. Any change to the mathematical model has to be done very carefully and has to be explained very well. Most of the time, when changing a part of the system in simulation, it well lead to serious control problems. It can also lead to system failure.
In ‘PID Parameter Optimization of an UAV Longitudinal Flight Control System’, Kamran Turkoglu, Ugur Ozdemir, Melike Nikbay, and Elbrous M. Jafarov, World Academy of Science, Engineering and Technology 21, 2008, pp. 340-345, According to which “an automatic control system design based on Integral Squared Error (ISE) parameter optimization technique has been implemented on longitudinal flight dynamics of an UAV. It has been aimed to minimize the error function between the reference signal and the output of the plant. In the article, objective function has been defined with respect to error dynamics. An unconstrained optimization problem has been solved analytically by using necessary and sufficient conditions of optimality, optimum PID parameters have been obtained and implemented in control system dynamics.”
Drawbacks make these controllers and/or algorithms used in these controllers less suited for continuous systems, or make these controllers complicated because of the number of loops that these controllers require. For instance, the number of gains and other settings that need to be tuned in know controllers makes these controllers difficulty, if not impossible, to apply to real processes.